Device and method for calibrating and improving the accuracy of barometric altimeters with GPS-derived altitudes

Abstract

A portable, handheld electronic navigation device includes an altimeter and a gps unit. An internal memory stores cartographic data, for displaying the cartographic data on a display of the navigation device. Accordingly, the device is capable of displaying cartographic data surrounding a location of the unit as determined by gps and altitude information as determined by the barometric altimeter and gps. The device provides an enhancement of the calibration and hence the accuracy of barometric altimeter measurements with the aid of derived altitudes from a gps. The device is able to determine the need for calibration and perform the subsequent computations necessary to facilitate the calibration. Furthermore, the device is able to determine a correction quantity that should be applied to barometric altitude readings, thereby allowing the device to be calibrated while in motion. Both of these features ultimately result in a more accurate determination of altitude. In accordance with an aspect of the invention, the altimeter of the navigation device may be calibrated with altitude information entered by a user, with altitude information obtained from the cartographic, with altitude information derived from gps or with any combinations thereof.

Description

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to enhancements for incorporation into an electronic navigation device. More particularly, the present invention is directed to an enhancement of the calibration and hence the accuracy of barometric altimeter measurements with the aid of derived altitudes from a global positioning system.

2. Description of the Related Art

In general, altitude measurements are made using two methods of measurement. One method utilizes a barometric altimeter. Barometric altimeters are devices that sense local atmospheric pressure and use a standard model of the atmosphere to convert this pressure measurement into altitude. Altitude measurements are referenced to height above mean sea level (MSL).

It is well known that local atmospheric pressure at a given altitude varies widely due to the effects of weather, solar heating, and other factors. Thus, in order to provide an accurate altitude, barometric altimeters must be calibrated to correct for these variations. The Global Positioning System (GPS) is a worldwide navigation system that can determine a user's position in horizontal and vertical dimensions. However, GPS vertical measurements are currently all referenced to the WGS-84 ellipsoid, a purely mathematical construct that approximates the shape of the earth. The GPS receiver must use a model that relates the height above the ellipsoid to the height above mean sea level.

Further, it is well known that the vertical measurement of a GPS system is inherently less accurate than the horizontal measurements. This is due to the fact that GPS satellites are constrained to be above the horizon for signal reception to occur. This geometry is less that optimal for measuring the vertical component of a user's location simply because there can not be satellites visible below the user (an optimal configuration would have satellites above and below the user). All GPS receivers are able to take into account satellite geometry (Dilution of Precision) and estimates of other satellite-related errors (URA) and provide a statistical estimate of the errors in the horizontal and vertical measurements.

In practice, a barometric altimeter typically provides a more stable measurement of altitude than GPS over short time periods. However, over long time periods, pressure variations can be of such magnitude that the barometric altimeter measurement of altitude is less accurate than the GPS measurement. The pressure-indicated altitude of an uncalibrated barometric altimeter is typically in error by many tens of meters due to normal atmospheric pressure fluctuations, weather fronts and other sources. However, this error is of a bias like nature--it is slowly varying with time--resulting in less accurate barometric altimeter readings over long time periods. Accordingly, while an altitude determination derived from barometric pressure may be meaningfully accurate in a short time frame, over time, the accuracy of such a determination becomes undesirable. Conversely, because GPS derived altitude suffers from different complementary errors, over a short time period (typically minutes time frame), GPS altitude measurements are subject to much larger variations than barometric altimeter measurements.

In an attempt to overcome the foregoing, one proposal combines a GPS unit and a barometric pressure sensor in the same housing. However, in that proposal the pressure sensor is used to augment GPS derived altitude information.

In particular, McBurney et al., in U.S Pat. No. 6,055,477, disclose a method of combination or integration of measurements made using two systems to provide better availability or accuracy in altitude measurements by estimating a barometric bias using the difference in altitude obtained from the two sources. The McBurney et al. method however, fails to recognize that utilizing the difference between a GPS derived altitude and a barometric altimeter altitude, as a term in calibrating the barometer altimeter, will common mode out any dynamic changes due to movement of the barometric altimeter and the GPS in tandem. As a result, the McBurney et al. approach undesirably requires that the user not change altitude during calibration periods.

Additionally, in the stated prior approach, an altimeter may only be calibrated using GPS derived altitude information when the user is stationary, often referred to as a "Calibration mode". The present invention makes no distinction between "calibration mode" and "navigation mode", indeed the barometric error is constantly being estimated and used to calibrate the system. Furthermore, the present invention provides a method to statistically determine the need for calibration which results in both the calibration and error estimation numbers being calculated and utilized without any user intervention, i.e. the user need not place the device in `calibration mode` to obtain the required bias parameter for calibration computations.

There exists a need for a method to take advantage of the long term stability of the GPS altitude measurement and the short term stability of the barometric altimeter measurement to produce an altitude measurement that is stable and accurate over long and short time periods. Additionally, the need exists for a method that uses both GPS-derived altitude and barometric altimeter altitude to produce an altitude measurement that is more stable and accurate than either measurement taken alone. The need also exists for an improved method to calibrate a barometric altimeter and to compute a barometric altitude correction quantity. Particularly, the need exists for a method to have GPS and altimeter outputs to be self calibrating while the user is on the move. The present invention fills the foregoing identified needs, and other needs, while overcoming the drawbacks of the prior art.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide an improved navigation unit.

It is another an object of this invention to compute a barometric altitude correction quantity such that the use of this quantity during calibration results in measurements that are more stable and accurate than GPS or barometric measurements taken independently.

It is another object of the present invention to improve on constraints that are required by prior art methods of various types where there is a user distinction between calibration and navigational modes.

It is a further object of the invention to provide unique options for statistically determining the need for calibration of an altimeter based on discrepancy between GPS altitude measurements and other altitude measurements.

It is another object of the present invention to provide continuous calibration of an altimeter while the unit is on the move.

These and other objects are achieved by a portable unit having an internal processor. Connected to the processor are, at a minimum, an input (such as a keypad), a display, a memory, a barometric pressure sensor, and a GPS receiver, which also connects to an antenna, such as a GPS patch antenna. These components, along with a power supply (such as batteries) are housed within a housing. As will be understood and appreciated, the input and display are accessible at an exterior of the housing, in a conventional manner.

A navigation device incorporating the present invention serves as a GPS unit, in that GPS signals from a plurality of satellites may be received by the GPS receiver, such that the processor calculates position information based upon the received signals. The conventional use and operation of GPS units is well known, and need not be further described.

Additionally, the present invention addresses an altimeter. In particular, the barometric pressure sensor measures barometric pressure and provides the sensed barometric pressure information to the processor. The processor, utilizing stored software, then converts the measured pressure into an altitude, which may be displayed or otherwise communicated to the user. The conversion of barometric pressure to altitude may be accomplished in any desired and conventional manner. For example, a lookup table may be provided in the memory, where the table contains altitude information corresponding to known barometric pressures. Thus, an altitude corresponding with a sensed barometric pressure may be retrieved from memory and displayed on the display. Alternatively and preferably, altitude (or elevation) may be calculated using a known equation.

In particular, the present invention provides a unique navigation device and method for a navigation device that combines data from a plurality of sensors and position information obtained from a GPS, to provide the user with an accurate representation of altitude information. Additionally, as stated, the simultaneous access to GPS information and altimeter information, as well as calculating the difference between that information to obtain an indication of bias, allows for features such as automatic calibration and calibration while the user is on the move.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and features of the invention noted above are explained in more detail with reference to the drawings, in which like reference numerals denote like elements, and in which:

FIG. 1 is a front view of an illustrative embodiment of a navigation of the present invention;

FIG. 2 is an illustrative block diagram of a navigation device that incorporates the present invention;

FIG. 3 is a graphical representation of a user's elevation trajectory and GPS based and barometric altimeter based elevation readings;

FIG. 4 is a block diagram illustrating barometric altimeter calibration processing according to the present invention; and

FIG. 5 is a block diagram illustrating barometric altimeter calibration processing according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

With reference initially to FIG. 1, a navigation device that incorporates the present invention is denoted generally by reference numeral 10. Navigation device 10 has a housing 12, a display 14, and an input 16, preferably a keypad input. Other known inputs, such as a touch screen, may be utilized additionally or alternatively. The housing 12 is preferably sized to be portable, although the invention is not limited to portable units.

With reference to FIG. 2, navigation device 10 has a processor 18. Connected to processor 18 are a memory 20, the display 14, the input 16 and a barometric pressure sensor 22. Additionally, a GPS receiver 24 is connected to the processor 18. An antenna 26, for receiving GPS signals, is connected to the GPS receiver 24. A power source, such as batteries, or a battery pack (not shown), is utilized to supply power to the various electronic components. Additionally, navigation device 10 may include a port, such as serial data port, for connecting the device 10 to a remote processor or personal computer for uploading information (such as map information) to the device 10, or for downloading information (such as route information) to a remote processor or personal computer. Alternatively, the device 10 may include wireless communication capabilities, such that data is received wirelessly from a remote site. As will be understood and appreciated, the various electronic components are housed within the housing 12, such that display 14 and keypad input 16 are accessible at an exterior of the housing.

With reference to FIG. 3, a graphical representation representing a user's elevation trajectory (A), a GPS elevation reading (B), and a barometric altimeter reading (C) is illustrated. In particular, an exemplary elevation profile of a user using navigation device 10 is represented by line A in FIG. 3 In other words, line A represents the trajectory of a user using navigation device 10 as, for example, he or she travels over terrain. The GPS elevation reading is depicted by signal B. As illustrated, the GPS elevation reading B varies over time, as the user moves along the path of use. Similarly, the barometric altimeter reading varies over time from the actual elevation trajectory of the user, although far less significantly, typically, than the GPS elevation reading. Additionally, as illustrated, the barometric altimeter reading is typically offset by a bias amount, or difference D, from the GPS elevation reading.

With reference now to FIGS. 4 and 5, a method of calibrating the altimeter of device 10 is illustrated and described. In accordance with an aspect of the invention, when the processor determines that the difference between the altitude based upon a barometric pressure reading from sensor 30 and GPS derived altitude differs by a selected threshold amount, the processor begins computations necessary to calibrate the barometric readings. In practice, a barometric altimeter typically provides a more stable measurement of altitude than GPS over short time periods (from tens of minutes to several hours). However, over long time periods, pressure variations can be of such magnitude that the barometric altimeter measurement of altitude is less accurate that the GPS measurement. As such, the processor must determine the appropriate altitude, utilizing a combination of these measurements. In particular, as indicated at step 30, the device is started up and initialized. Processing advances to step 32, at which the processor 18 measures the difference between an elevation reading provided from the barometric altimeter and an elevation reading provided by the GPS unit. Processing advances to step 34, at which the processor 18 computes the average between that difference. As will be understood and appreciated, upon the initial measurement at step 32, the average difference will simply be equal to the difference. As will be further understood, and appreciated, GPS based and barometric altimeter based measurements are taken continuously, or periodically, and on additional passes through this processing loop, the additional information at step 34 is averaged recursively, although other averaging techniques may be employed.

As processing advances to step 36, processor 18 computes the uncertainty of the average difference determined at step 34. A decision statistic is employed to make the decision to use the estimated barometer altimeter difference to calibrate the baroaltimeter reading.

At step 38, the processor 18 determines an average barometer drift for an elapsed time associated with the computed average difference determined at step 34. In particular, memory 20 preferably has, in table form, the average drift of the barometer over time. Following step 38, the processing advances to step 40. At step 40, processor 18 determines whether the uncertainty of the computed average difference, σ_ΔHave(k), as determined at step 36, is significantly less than the uncertainty due to baro drift, σ_Baro, as obtained at step 38. In particular, it is determined whether the uncertainty of the computed average difference is less than the determined average barometer drift by a selected threshold. When the uncertainty of the computed average difference σ_ΔHave(k) is not less than σ_Baro by at least the threshold amount, processing returns to step 32, so that the processor may continue taking difference measurements. When, however, it is determined at step 40 that the uncertainty of the computed average difference is less than a preselected threshold than the obtained average barometer drift, processing advances to step 42.

At step 42, processor 18 determines whether the elapsed time, that being the time associated with the measurements taken thus far, is greater than a scaled correlation time of GPS vertical errors. When it is determined at step 42 that the elapsed time is not greater than a scaled correlation time of GPS vertical errors, processing returns to step 32. When, however, it is determined that the elapsed time is greater than a scaled correlation time of GPS vertical errors, processing advances to step 44. At step 44, the processor 18 calibrates the barometric altimeter, pursuant to the processing flow of FIG. 5 as discussed below. Following the calibration at step 44, processing advances to step 46, where the error statistics of the average difference are reinitiated based upon calibrated barometric altimeter and processing then returns to step 32.

With reference particularly to FIG. 5, calibration of the barometric altimeter is illustrated and described. At step 48 of FIG. 5, the device 10 of the present invention computes a preliminary calibrated barometrically determined elevation.

The calibration process begins with obtaining a calibrated barometric altitude H_B,cal by subtracting the estimated calibration altitude difference from the current barometric altitude i.e. H_B,cal (t)=H_B(t)-ΔHH_cal to remove the bias and thus approximate the true altitude H_T(t). This is the approach taken in prior art which is not an optimal approach. The present invention goes further than prior art, by using the calibrated barometric altitude H_B,cal to compute a base calibration pressure P_B,cal, which is then used to compute local altitude H_B.

Processing then advances to step 50, where the processor 18 computes a calibrated base pressure value P_B. The calibrated base pressure P_B is determined by solving the following equation for P_B, identified as P_B,cal. $P_{B,\mathrm{cal}}=\frac{P_L}{{\left[\frac{H_{B,\mathrm{cal}}*L}{T_O}+1\right]}^{\frac{-g}{\mathrm{RL}}}}$

At step 52, processor 18 computes a calibrated barometric elevation for use in subsequent measurements. In particular, a calibrated barometric elevation, utilizing the computed calibrated base pressure value, P_B,cal, is determined according to the following equation: $H_{B,\mathrm{cal}}=\frac{T_O}{L}\⁢\left[{\left(\frac{P_L}{P_{B,\mathrm{cal}}}\right)}^{\frac{-\mathrm{RL}}{g}}-1\right]$

The calibrated barometric elevation is then displayed on display 14 of device 10, and used in further processing.

Accordingly, the present invention employs a method of estimating the barometric bias using the difference term ΔH_ave to common mode out any dynamical changes due to movement of the baro-altimeter and the GPS in tandem which is unique with respect to known methods. In other words, the method of the present invention accounts for the fact that changes in altitude by a user are reflected in both the barometric altitude reading and the GPS altitude reading, thereby allowing calibration to take place while the baro-altimeter and GPS are in motion. A user is not constrained to be motionless during the "calibration mode". Furthermore, this method allows the barometric error to be continuously estimated and used to calibrate the system when such a need is determined by the previously discussed calibration decision model.

The best known mode for carrying out the present invention utilizes models as described below.

Barometric model

The barometric altitude is modeled by

H_B(t)=H_T(t)+B_B(t)+Q_B(t) (1)

where H_B(t) is the barometric pressure indicated altitude, H_T(t) is the true altitude in MSL, B_B(t) is a slowly varying bias-like term, and Q_B(t) is a zero mean Gaussian noise term of variance σ_Q². Equation (1) shows that indicated barometric altitude is the sum of the true altitude, plus a bias-like term that is due to the pressure variation of local pressure from the standard atmospheric model, and a noise term that is due to noise of the sensor, A/D, quantization, and other sources.

In order to calibrate the barometric altimeter, the bias term B_B(t) must be determined.

GPS Model

GPS altitude is modeled by

H_G(t)=H_T(t)+B_G(t)+C_G(t) (2)

where H_G(t) is the GPS altitude (in MSL), B_G(t) is a slowly varying bias term due to ionospheric errors, ephemeris errors, satellite clock errors, and other factors, and C_G(t) is a zero mean correlated noise term of a much shorter time constant than either B_G(t) or B_B(t). The variance of the C_G(t) process is σ_V,GPS² and is an estimate of the errors associated with the vertical channel. When Selective Availability was in operation, C_G(t) was the largest contributor to GPS altitude error (by far). Also, the B_G(t) term is typically much smaller magnitude than the B_B(t) term.

Determining Error in Barometric Altitude

One approach to calibrating the baro-altimeter using GPS is to simply perform a difference of equations (1) and (2) at a particular point in time where certain statistical rules (to be discussed later) are met.

H_B(t)-H_G(t)=ΔH(t)=B_B(t)+Q_B(t)-B_G(t)-C_G(t) (3)

Taking the expected value of ΔH(t) yields

ΔH(t)=B_B(t)-B_G(t) (4)

since the expected value of terms Q_B(t) and C_G(t) is zero (they are zero mean noise processes).

One can then calibrate the baro-altimeter using ΔH(t). Calibration is discussed in more detail later.

Because Q_B(t) and C_G(t) are zero mean random processes, one can reduce the error involved estimating B_B(t) by averaging ΔH(t) over many samples. Note that B_G(t) is ignored since it is typically small. When estimating a random bias in the presence of additive noise, the variance of the estimate is reduced by the number of samples used to form the estimate only if the additive noise is uncorrelated. Q_B(t) is indeed uncorrelated Gaussian noise. However C_G(t) is correlated noise with a correlation time of τ_C. Therefore the estimation error is treated differently.

One can recursively average ΔH(t) over many samples according to eqn. (5). $\begin{array}{cc}\mathrm{\Δ}\⁢\⁢H_{\mathrm{ave}}\⁡\left(k\right)=\frac{k-1}{k}\⁢\⁢\mathrm{\Δ}\⁢\⁢H_{\mathrm{ave}}\⁡\left(k-1\right)+\frac{1}{k}\⁢\⁢\mathrm{\Δ}\⁢\⁢H\⁡\left(t_n\right)& \left(5\right)\end{array}$

The uncertainty of this average estimate is the root sum square of uncertainty reduction in σ_Q and the uncertainty reduction to σ_V,GPS eqn. (6). $\begin{array}{cc}{\mathrm{\σ}}_{\mathrm{\Δ}\⁢\⁢H_{\mathrm{ave}}}\⁡\left(k\right)={\left(\frac{{\mathrm{\σ}}_Q^2}{k}+\frac{{\mathrm{\σ}}_{V,\mathrm{GPS}}^2}{1+\frac{k*\mathrm{\Δ}\⁢\⁢t}{{\mathrm{\τ}}_C}}\right)}^{1/2}& \left(6\right)\end{array}$

where k is the number of samples in the average, and Δt is the interval between samples.

Note that the contribution of a σ_V,GPS is reduced according to the correlation time of this process. What this means is that essentially one correlation time must elapse before samples of the C_G(t) process are sufficiently decorrelated to contribute an uncertainty reduction equivalent to an independent sample.

It is noted that σ_V,GPS is a dynamically changing function, whereas σ_Q is a quantity that is chosen a-priori. To accommodate these dynamics, σ_V,GPS is also recursively computed over the estimation interval. Again, the same weighting function as used in eqn. (5) is used here $\begin{array}{cc}{\mathrm{\σ}}_{V,\mathrm{ave}}\⁡\left(k\right)=\frac{k-1}{k}\⁢\⁢{\mathrm{\σ}}_{V,\mathrm{ave}}\⁡\left(k-1\right)+\frac{1}{k}\⁢\⁢{\mathrm{\σ}}_{V,\mathrm{GPS}}\⁡\left(t_n\right)& \left(7\right)\end{array}$

Then σ_V,ave(k) is substituted into equation (6). $\begin{array}{cc}{\mathrm{\σ}}_{\mathrm{\Δ}\⁢\⁢H_{\mathrm{ave}}}\⁡\left(k\right)={\left(\frac{{\mathrm{\σ}}_Q^2}{k}+\frac{{\mathrm{\σ}}_{V,\mathrm{ave}}^2\⁡\left(k\right)}{1+\frac{k*\mathrm{\Δ}\⁢\⁢t}{{\mathrm{\τ}}_C}}\right)}^{1/2}& \left(8\right)\end{array}$

Equation (8) is the final form of the uncertainty estimate for ΔH_ave.

Decision Statistics For Calibrating Baro-Altimeter

The primary decision statistic to use the baro-altimeter difference estimate ΔH_ave(k) to calibrate the baro-altimeter is when

σ_ΔHave(k)<β*σ_Baro(t_n-t^cal) (9)

where β is any non-negative constant and σ_Baro(t_n-t_cal) is an estimate of the uncertainty to the baro-altimeter. σ_Baro is a function of the time that has elapsed since the last calibration and the uncertainty of the calibration. Furthermore, k is constrained so that

k*Δt>α*τ_C (10)

which constrains the averaging period to be some multiple α of the correlation time of C_G(t).

Also, B_B(t) does vary slowly with time. If the averaging period exceeds another time threshold, t_max, the assumption that B_B(t) is constant does not hold, and the averaging process is re-initialized.

Once the calibration decision statistics have been met, ΔH_ave(k) is set equal to ΔH_cal, and all recursive estimation algorithms are re-initialized, in particular σ_Baro is set equal to σ_ΔH,ave.

Approach to Calibrating Barometric Altimeter

The simplest approach to calibration of the baro-altimeter is to simply subtract ΔH_cal from the current barometric altitude

H_B,cal(t)=H_B(t)-ΔH_cal (11)

This removes the bias B_B(t) and thus H_B,cal(t) approximates H_T(t), the true altitude.

Indeed, this is the approach taken in McBurney, et al. However, this is not the optimal approach. A fundamentally different approach to baro calibration is used in this invention.

Standard Atmosphere Model Relating Pressure to Altitude

For altitude below 11,000 meters, the following equation is used to compute altitude from pressure. $\begin{array}{cc}{H}_B=\frac{T_O}{L}\&it;\left[{\left(\frac{P_L}{P_B}\right)}^{\frac{-\mathrm{RL}}{g}}-1\right]& \left(12\right)\end{array}$

where the following quantities are define in the 1993 ICAO Standard Atmosphere Model.

T_O=Standard Temperature at Sea Level

L=Lapse rate

R=Gas Constant

g=Acceleration of Gravity

P_L=Local Pressure (measured by barometer)

P_B=Base pressure (in this case pressure at Sea Level)

H_B=Local pressure altitude

From eqn. (12), it is shown that the model that relates pressure to altitude is an exponential model, not a linear model. Thus, if one determines that ΔH_cal is 30 meters at a nominal altitude of 500 meters, it does not hold that the proper calibration factor will still be 30 meters at a nominal altitude of 5000 meters. The reason is that 30 meters of elevation difference at 500 meters nominal altitude is a far greater pressure differential than 30 meters of elevation difference at 5000 meters nominal altitude. The calibration method employed in the invention accounts for this discrepancy.

BAROMETRIC ALTIMETER CALIBRATION TECHNIQUE

In this invention during calibration P_L is treated as a constant and P_B is allowed to vary. The newly computed P_B,cal (see eqn. 13) is used in subsequent altitude computations in equation (12).

P_B,cal is computed as shown in equation (13). H_B,cal is the calibrated barometric altitude estimated using GPS. $\begin{array}{cc}{P}_{B,\mathrm{cal}}=\frac{P_L}{{\left[\frac{H_{B,\mathrm{cal}}*L}{T_O}+1\right]}^{\frac{-g}{\mathrm{RL}}}}& \left(13\right)\end{array}$

SUMMARY OF BAROMETRIC ALTIMETER CALIBRATION PROCESS

1. Evaluate equations (9) and (10) to determine if calibration decision statistics are met.

2. If so, compute H_B,cal according to eqn. (11).

3. Compute P_B,cal according to eqn. (13).

4. Begin computing H_B according to (12) using P_B,cal.

5. Set σ_Baro to σ_ΔH,ave.

6. Resume estimation of ΔH_ave and σ_V,ave(k) using eqns. (5-7).

From the foregoing it will be seen that this invention is one well adapted to attain all ends and objects herein above set forth together with the other advantages which are obvious and which are inherent to the structure.

It will be understood that certain features and sub-combinations are of utility and may be employed without reference to other features and sub-combinations. This is contemplated by and is within the scope of the claims.

Since many possible embodiments may be made of the invention without departing from the scope thereof, it is to be understood that all matter herein set forth or shown in the accompanying drawings is to be interpreted as illustrative, and not in a limiting sense.

Claims

1. A navigation device comprising:

a barometric altimeter for providing a barometric based evaluation reading:

a gps unit for providing a gps based evaluation reading, wherein a difference between said barometric based elevation reading and said gps based elevation reading is determined, and wherein said difference is used to calibrate said barometric altimeter;

wherein said difference is an average difference between said barometric based elevation reading and said gps based elevation reading over a period of time; and

wherein said device has a memory having stored therein data indicative of an accuracy drift associated with said barometric altimeter over a period of time, and wherein said device determines whether for an elapsed time period, said uncertainty of the average difference between said barometric based altimeter and said gps based altimeter is less, by a predetermined amount, than said accuracy drift.

2. The navigation device as set forth in claim 1, wherein when said uncertainty of the average difference is less by a predetermined amount than said accuracy drift, said device determines whether said elapsed time is greater than a determined value corresponding to a correlation time associated with gps vertical errors.

3. The navigation device as set forth in claim 2, wherein when said elapsed time is greater than a determined value corresponding to a correlation time associated with gps vertical errors, then said navigation device calibrates said barometric altimeter using said difference between said barometric based elevation reading and said gps based elevation reading.

4. The navigation device as set forth in claim 3, wherein said device (a) computes a preliminary calibrated elevation for said altimeter based upon said barometric based elevation reading and said difference between said barometric based elevation reading and said gps based elevation reading, (b) computes a base pressure value corresponding to said preliminary calibrated elevation and (c) computes a fully-calibrated elevation.

5. The navigation device as set forth in claim 4, wherein said device has a display, and wherein said fully calibrated elevation is displayed on said display.

6. The navigation device as set forth in claim 4, wherein, when said device takes an additional gps based and barometric based elevation reading, said device uses said fully-calibrated elevation in computing a difference between said additional readings.

7. A method for determination of an uncertainty estimate for an error in barometric altitude (ΔH_ave) comprising the steps of:

receiving gps signals from a plurality of gps satellites and computing a gps altitude modeled by h_G(t)=H_T(t)+B_G(t)+C_G(t), where h_G(t)is the gps altitude (in MSL), B_G(t) is a slowly varying bias term due to ionospheric errors, ephemeris errors, satellite clock errors and other factors, and C_G(t) is a zero mean correlated noise term of a much shorter time constant than either B_G(t) or B_B(t) a slowly varying bias like term relating to barometric pressure;

computing a barometric altitude reading modeled by h_B(t)=H_T(t)+B_B(t)+Q_B(t) where h_B(t) is the barometric pressure indicated altitude, h_T(t) is the true altitude in MSL, B_B(t) is a slowly varying bias like term, and Q_B(t) is a zero mean Gaussian noise term of variance σ_Q²;

determining the difference between barometric and gps derived altitude ΔH(t) as a function of time;

continuously estimating the average altitude difference ΔH_ave over multiple samples by recursively averaging ΔH(t) over many samples according to the equation $\mathrm{\&Delta;}\&it;\&it;{\mathrm{<span\; class="c3\; g0">h</span>}}_{\mathrm{ave}}\&it;\left(k\right)=\frac{k-1}{k}\&it;\&it;\mathrm{\&Delta;}\&it;\&it;{\mathrm{<span\; class="c3\; g0">h</span>}}_{\mathrm{ave}}\&it;\left(k-1\right)+\frac{1}{k}\&it;\&it;\mathrm{\&Delta;}\&it;\&it;\mathrm{<span\; class="c3\; g0">h</span>}\&it;\left(t_n\right);$

estimating the uncertainty of the gps vertical Channel σ_V,ave(k) by recursively computing over the estimation interval, the uncertainty of the gps Channel as a function of time according to the equation ${\mathrm{\&sigma;}}_{V,\mathrm{ave}}\&af;\left(k\right)=\frac{k-1}{k}\&it;\&it;{\mathrm{\&sigma;}}_{V,\mathrm{ave}}\&af;\left(k-1\right)+\frac{1}{k}\&it;\&it;{\mathrm{\&sigma;}}_{V,\mathrm{<span\; class="c22\; g0">gps</span>}}\&af;\left(t_n\right);\mathrm{and}$

computing the root sum square of uncertainty reduction in σ_Q and the uncertainty reduction to σ_V,gps to yield the uncertainty of the average estimate according to the equation ${\mathrm{\&sigma;}}_{\mathrm{\&Delta;}\&it;\&it;{\mathrm{<span\; class="c3\; g0">h</span>}}_{\mathrm{ave}}}\&af;\left(k\right)={\left(\frac{{\mathrm{\&sigma;}}_Q^2}{k}+\frac{{\mathrm{\&sigma;}}_{V,\mathrm{<span\; class="c22\; g0">gps</span>}}^2}{1+\frac{k*\mathrm{\&Delta;}\&it;\&it;t}{{\mathrm{\&tau;}}_C}}\right)}^{1/2}.$

8. A method for the calibration of a barometric altimeter comprising the steps of:

evaluating calibration decision criteria for the need to calibrate the barometric altimeter based on the equation σ_ΔHave(k)<β*σ_Baro(t_n-t_cal), where β is any non-negative constant and σ_Baro(t_n-t_cal) is an estimate of the uncertainty to the barometric altimeter, as a function of the elapsed time since the last calibration and σ_ΔH,ave is the uncertainty of the average difference between barometric altitude and gps derived altitude;

constraining the averaging period to be some multiple α of the correlation time of the signal noise term C_G(t) in the following manner k*Δt>α*τ_C;

computing a base pressure P_B,cal and a local pressure altitude h_B wherein, $P_{B,\mathrm{cal}}=\frac{P_L}{{\left[\frac{{\mathrm{<span\; class="c3\; g0">h</span>}}_{B,\mathrm{cal}}*L}{T_O}+1\right]}^{\frac{-g}{\mathrm{RL}}}};$

defining the barometric uncertainty error as the uncertainty estimate for the average difference in altitude values between the barometric altimeter and gps i.e. σ_Baro=σ_ΔH,ave; and

calibrating the barometric altimeter measurement h_B using an atmospheric model relating base pressure P_B,cal to altitude in the equation ${\mathrm{<span\; class="c3\; g0">h</span>}}_B=\frac{T_O}{L}\&it;\left[{\left(\frac{P_L}{P_{B,\mathrm{cal}}}\right)}^{\frac{-\mathrm{RL}}{g}}-1\right].$

9. A method of calibrating a barometric altimeter, said method comprising the steps of:

obtaining a barometric altitude reading h_B(t) as a function of time using a barometric model;

calculating a gps signal based altitude h_G(t) as a function of time using a gps model;

computing a differential altitude ΔH(t), between the gps based altitude and the barometric altimeter based altitude, as a function of time; and

calibrating the barometric altimeter by adding or subtracting, from the barometric altimeter reading, the differential altitude.

10. The method as recited in claim 9 wherein, a decision to calibrate is first determined based on a decision statistic comprising the steps of:

computing an uncertainty factor σ_Baro as a function of elapsed time since the last calibration;

computing a product of the uncertainty factor and a non-negative constant β; and

comparing said product with the uncertainty associated with an averaging period for the altitude differential.

11. The method as recited in claim 9, further comprising the steps of:

computing a base pressure P_B,cal at the time of calibration;

computing a barometric altitude h_B using said base pressure;

re-initializing recursive estimations; and

continuously estimating an average altitude differential and recursively estimating the uncertainty of the average altitude differential.

12. A method of calibrating a barometric altimeter, said method comprising the steps of:

obtaining a barometric altitude reading h_B(t) as a function of time using a barometric model;

obtaining a barometric altitude reading h_B(t) as a function of time;

calculating a correction term U(t_k) using a state feedback filter; and

calibrating a barometric altimeter by adding said correction term to the barometric altitude reading.

13. A method as recited in claim 12, further comprising the steps of:

computing a calibrated barometric altimeter reading h_B,cal;

computing a base pressure P_B,cal at the time of calibration using said calibrated barometric altitude; and

computing a barometric altitude using said base pressure in a Standard Atmosphere Model.